Untangling harvest‐streamflow responses in foothills conifer forests: Nexus of teleconnections, summer‐dominated precipitation, and storage
Bibliographic record
Abstract
Abstract This study re‐evaluated data from the historical Tri‐Creeks Experimental Watershed (1967–1988) in Alberta, Canada to address the initial question of forest harvest effects on streamflow and investigate the potential influence of teleconnections, summer‐dominated precipitation, and watershed storage on runoff generation. Tri‐Creeks has deep (up to 21 m) glacial deposits underlain by folded and faulted sedimentary bedrock with considerable potential for subsurface water storage. Timing of the conifer forest harvest experiment in two sub‐watersheds (>50% harvested) and one reference occurred near the 1976–77 Pacific Decadal Oscillation (PDO) phase change that led to less snowfall, but little difference in annual precipitation or runoff between phases after harvest. Established statistical and hydrological modelling methods that used regression techniques of observed and simulated streamflow to separately analyse sub‐watersheds did not detect change in average daily or annual runoff due to harvest. The interannual hydroclimatic variability influenced by the climate shift, attenuation of summer precipitation by the drier antecedent conditions in the warm period following harvest, and large potential for subsurface water storage contributed to shifts in runoff and uncertain detection of streamflow response. However, a hydrological modelling approach using calibrated parameters separately in the pre‐ and post‐harvest periods indicate significant change in rainfall‐generated peak runoff events and summer runoff following harvest, which was not detected in the reference watershed. Model calibration required less soil storage capacity in the treated watersheds in the post‐harvest period compared to the reference likely due to reduced transpiration that increased the likelihood of storm runoff during larger summer rainfall events. Within the context of streamflow responses to harvest in conifer dominated forest landscapes with seasonal snow cover, this study illustrates how complexity of climate variability and interaction with watershed storage and continental summer‐dominated precipitation may confound and mask the interpretation of harvest effects in paired‐watershed studies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".